1. Acquiring and Using World Knowledge using a Restricted Subset of English Peter Clark, Phil Harrison, Tom Jenkins, John Thompson, Rick Wojcik Boeing Phantom Works, Seattle

2. Introduction Knowledge acquisition is still a major bottleneck –automated methods are good but still very restricted Our approach: –Knowledge entry using Controlled Language –Hits “sweet spot” between logic and full NLP –language interpreter generates logic output Outline: 1.Our Controlled Language Processing technology 2.Discussion on Natural Language as a basis for KR

3. Formal language Unrestricted natural language Controlled English “Consider the following possible situation in which a ball first…” too hard for the computer to understand “A ball falls from a cliff” “  x  y B(x)  R(x,y)  C(y)” too hard for the user The Language Spectrum

4. An object is thrown with a horizontal velocity of 20 m/s from a cliff that is 125 m above level ground. If air resistance is negligible, how long does it take the object to fall to the ground? CPL (Computer-Processable Language) Original text (incomprehensible to computer): An object is thrown from a cliff. The horizontal velocity of the object is 20 m/s. The top of the cliff is 125 m above level ground. The object falls 125 m to the ground. What is the duration of the fall? Rewritten in CPL (computer can understand): Short sentencesNo pronouns Simple sentence structures

5. Target Interpretation Sentences in first-order logic Capable of supporting machine inference “An object is thrown from a cliff” ObjectCliff Throw object origin isa(_Object1, object_n1) isa(_Cliff2, cliff_n1) isa(_Throw3, throw_v1) object(_Throw3, _Object1) origin(_Throw3, _Cliff2)

6. Target Interpretation Sentences in first-order logic Capable of supporting machine inference “a person is carrying an entity that is inside a room” PersonObject Carry agent object isa(_Person1, person_n1) isa(_Room2, room_n1) isa(_Entity3, entity_n1) isa(_Carry4, carry_v1) object(_Carry4, _Entity3) agent(_Carry4, _Person1) is-inside(_Entity4, _Room2) =====> is-inside(_Person1, _Room2) Room is-inside “the person is in the room.” IF THEN

7. Overview of Processing “An object is thrown from a cliff” Parser & LF Generator Word sense disambiguator Relational disambiguator Coreference identifier Structural reorganizer ObjectCliff Throw object origin (_Object13320 instance_of object_n1) (_Cliff13321 instance_of cliff_n1) (_Throw13319 instance_of throw_v1) (_Throw13319 object _Object13320) (_Throw13319 origin _Cliff13321) World Knowledge Linguistic Knowledge

8. Entering Quantified Expressions (Rules) Seven “rule templates” used: IF sentence THEN sentence ABOUT object: sentence object IS noun/verb phrase BEFORE sentence, sentence BEFORE sentence, it is not true that sentence AFTER sentence, sentence AFTER sentence, it is not true that sentence Processing: 1.Each sentence processed as a ground assertion 2.Quantifiers are added (Prolog-style) 3.“Action” templates become situation calculus rules

9. Original text An object is thrown from a cliff. The horizontal velocity of the object is 20 m/s. The top of the cliff is 125 m above level ground. CPL (Controlled english) LogicKB Overall Flow of Processing Paraphrase of system’s understanding An object is thrown from a cliff. The horizontal velocity of the object is 20 m/s. The top of the cliff is 125 m above level ground. Rewriting advice

17. Part II: Discussion Controlled Languages: Strengths and challenges

18. Strengths… CPL is easy to use, appears viable –built KB with over 1000 rules –KB is inference-capable easy to inspect and organize Makes knowledge entry accessible to many users – major achievement  x  y B(x)  R(x,y)  C(y)??? “A man is driving a truck towards the factory”

19. Challenges: 1. Reformulating in a Controlled Language is not trivial Task is not just grammatical reformulation Rather: – “natural” English leaves much knowledge implicit –CPL author must make that explicit “attack: intense adverse criticism” Original text: “IF a person attacks a 2 nd person THEN the first person criticizes the 2 nd person intensely.” CPL:

20. Challenges: 1. Reformulating in a Controlled Language is not trivial Task is not just grammatical reformulation Rather: – “natural” English leaves much knowledge implicit –CPL author must make that explicit “axis: the center around which something rotates” Original text: “IF an object is rotating THEN the object is turning around the object’s axis.” CPL:

21. 2. Users may not be aware of system’s mistakes 1.User must be able to spot misinterpretations easily –System’s paraphrase must be unambiguous 2.User must know how to correct them “The man ate the sandwich on the plate” “The man ate on the plate. He ate the sandwich.” ??????

22. 2. Users may not be aware of their mistakes User must be able to spot errors easily –System’s paraphrase must be unambiguous User must know how to correct them “The man ate the sandwich on the plate” “The man ate on the plate. He ate the sandwich.”“the man ate the sandwich that was on the plate”

23. 3. Natural-Language-based knowledge representations have limited expressivity “Natural language is very expressive” …not to the computer! (Avoid “wishful semantics”) Expressiveness = –the amount the computer understands –the amount it is able to use to draw conclusions from Everything else is meaningless to the computer e.g., CPL can’t express: –constraints, defaults, some quantification patterns

24. 4. Sometimes, linguistically motivated representations are poor Language-based KR: –Most concepts correspond to words –Structure of KB will mirror structure of language Is this bad? Sometimes… “… walked for 10 miles” “Traditional” KR distance(_Walk1, _Distance1) value(_Distance1, 10, mile) distance(_Walk1, _Mile1) count(_Mile1, 10) NL-based KR 

25. 5. (Lack of) Canonicalization Many ways to say the same thing System needs to realize the equivalence BUT: often NL-based KRs will not  Solutions: Add equivalence rules. (But there are lots!!) –e.g., “Conducting a X of Y ↔ Xing a Y” Have the interpreter normalize the input. Restrict the input language. “conducting a test of an entity” “testing an entity”

26. Summary CPL = a restricted English language for knowledge –Hits “sweet spot” between logic and full NLP –Produces inference-capable representations –Is viable, used to build a large KB But: No “free lunch” –requires skill to use it effectively NL-based KRs are becoming more important! –Web: need semantically meaningful annotations –AI: need better knowledge acquisition tools Some exciting possibilities ahead (esp. at Boeing!)